DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/28/2024 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 20 objected to because of the following informalities: disclosing first, second, and fourth metrics without third metrics. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 9 and 18 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 9 and 18 recites “mapping the graph to hardware of a distributed computing system based on the trained model.” It is unclear whether the graph refers to the first graph or the second graph in Claim 1. Specs [0008] applicant states “A method can include mapping a task graph to hardware of a distributed computing system based on a trained machine learning model.” Therefore, Examiner interprets “the graph” refers to the first graph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, Claims 1-9 is directed to a method and Claims 10-18 directed to a system claim. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
However, Claims 19-20 is directed to an article of manufacture (“computer readable medium”) that is signal per se. The specification does not provide a clear definition of whether the claimed medium is limited to statutory or non-transitory elements. Therefore, under the broadest reasonable interpretation, the claim element “computer readable medium” is not limited to statutory elements and can be considered non-statutory. Claim 19-20 is rejected because it does not fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 1:
2A Prong 1:
and generating, (This step for indicating an assignment of tasks to devices is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
obtaining a first graph corresponding to a computational process, the graph including one or more first nodes corresponding to respective tasks of the computational process, and one or more first edges between pairs of the first nodes, each of the first edges corresponding to respective output from a first task of the tasks to a second task of the tasks; (The step directed to obtaining information, which is understood to be insignificant extra- solution activity. See MPEP 2106.05(g).)
obtaining a second graph corresponding to a computer network architecture, the graph including one or more second nodes corresponding to processing constraints at particular devices of the computer network architecture, and one or more second edges between the nodes each corresponding to communication constraints between particular devices; (The step directed to obtaining information, which is understood to be insignificant extra- solution activity. See MPEP 2106.05(g).)
and generating, by a machine learning process, a trained model as output, the trained model obtaining as input a combination of the first graph and the second graph (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., assigning tasks) - see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
obtaining a first graph corresponding to a computational process, the graph including one or more first nodes corresponding to respective tasks of the computational process, and one or more first edges between pairs of the first nodes, each of the first edges corresponding to respective output from a first task of the tasks to a second task of the tasks; (This step is directed to obtaining information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity as storing and receiving information identified by the court (MPEP 2106.05(d)(ll)(IV)))))
obtaining a second graph corresponding to a computer network architecture, the graph including one or more second nodes corresponding to processing constraints at particular devices of the computer network architecture, and one or more second edges between the nodes each corresponding to communication constraints between particular devices; (This step is directed to obtaining information, which is understood to be insignificant extra-solution activity such as mere data gathering as discussed in MPEP 2106.05(g) and is well understood, routine and conventional activity as storing and receiving information identified by the court (MPEP 2106.05(d)(ll)(IV)))))
and generating, by a machine learning process, a trained model as output, (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., assigning tasks) - see MPEP 2106.05(f).)
the trained model obtaining as input a combination of the first graph and the second graph (This step is directed to obtaining information, which is understood to be insignificant extra-solution activity such as mere data gathering as discussed in MPEP 2106.05(g) and is well understood, routine and conventional activity as storing and receiving information identified by the court (MPEP 2106.05(d)(ll)(IV)))))
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 10: see the rejection of Claim 1 above. Same rationale applies.
2A Prong 2 & 2B: The claim recites another additional element “a memory and a processor to” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 19: see the rejection of Claim 1 above. Same rationale applies.
2A Prong 2 & 2B: The claim recites another additional element “A computer readable medium including one or more instructions stored thereon and executable by a processor to:obtain, by the processor,” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 2 and 11
2A Prong 1: wherein the generating is based on one or more first metrics each associated with computational factors of corresponding ones of the first nodes. (This step for generating based on metrics is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claim 3 and 12
2A Prong 1: wherein the generating is based on one or more second metrics each associated with processing factors of corresponding ones of the second nodes. (This step for generating based on metrics is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claim 4 and 13
2A Prong 1: wherein the generating is based on one or more third metrics each associated with output factors of corresponding ones of the first edges. (This step for generating based on metrics is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claim 5 and 14
2A Prong 1: wherein the generating is based on one or more fourth metrics each associated with bandwidth factors of corresponding ones of the second nodes. (This step for generating based on metrics is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claim 6 and 15
2A Prong 1: None
2A Prong 2 & 2B: wherein the first graph and the second graph each comprise a respective directed graph. (The specification of graph used is understood to be a field of use limitation – See MPEP 2106.05(h).)
Regarding Claim 7 and 16
2A Prong 1: None
2A Prong 2 & 2B: wherein the machine learning model comprises a graph convolutional network model. (The specification of model used is understood to be a field of use limitation – See MPEP 2106.05(h).)
Regarding Claim 8 and 17
2A Prong 1: None
2A Prong 2 & 2B: wherein the generating is based on an existing scheduling model as a teacher model to train the model. (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic graphical model as a tool to perform the abstract idea (i.e., assigning) - see MPEP 2106.05(f).)
Regarding Claim 9 and 18
2A Prong 1: mapping the graph to hardware of a distributed computing system (This step for mapping a graph to hardware of a system is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claim 20
2A Prong 1: generate the trained model based on one or more first metrics each associated with computational factors of corresponding ones of the first nodes, based on one or more second metrics each associated with processing factors of corresponding ones of the second nodes, and based on one or more fourth metrics each associated with bandwidth factors of corresponding ones of the second nodes. (This step for generating a trained model based on metrics is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 5-7, 9-12, 14-16, 18-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Huang et al. (“TATA: Throughput-Aware TAsk Placement in Heterogeneous Stream Processing with Deep Reinforcement Learning”, hereinafter “Huang”).
Regarding Claim 1
Huang discloses: A method comprising:
obtaining a first graph corresponding to a computational process, the graph including one or more first nodes corresponding to respective tasks of the computational process, and one or more first edges between pairs of the first nodes, each of the first edges corresponding to respective output from a first task of the tasks to a second task of the tasks; ([Huang, Page 45, Section A & Fig. 1; Page 46, Section A, Para 1; Page 47, Fig. 2] discloses obtaining a task graph (i.e first graph) corresponding to a task (i.e computational process). [Page 45, Section A, Para 1-3 & Fig. 1] discloses the task graph including nodes (i.e first nodes) corresponding to jobs (i.e respective tasks) of a task (i.e computational process). [Page 45, Section A, Para 2 & Fig. 1] discloses task graph as a DAG with edges (i.e first edges) between pairs of nodes, each edge corresponds to an output from a first job (i.e task) of jobs (i.e tasks) to a second job of jobs: “the tasks in ju communicates with tasks in jv.”))
obtaining a second graph corresponding to a computer network architecture, the graph including one or more second nodes corresponding to processing constraints at particular devices of the computer network architecture, and one or more second edges between the nodes each corresponding to communication constraints between particular devices; and ([Huang, Page 45-46, Section B; Page 46, Section A, Para 1; Page 47, Fig. 2] discloses obtaining resource graph (i.e second graph) corresponding to threads and processes of inter-host communications network (i.e computer network architecture). [Page 45-46, Section B & Fig. 1] discloses resource graph including nodes (i.e second nodes) corresponding to CPU and memory capacities (i.e processing constraints) of a host (i.e particular devices) of an inter-host communications network (i.e computer network architecture), and the edges (i.e second edges) between nodes correspond to communication delays between slots that are contained in hosts (i.e particular devices) as seen in Fig. 2)
generating, by a machine learning process, a trained model as output, the trained model obtaining as input a combination of the first graph and the second graph, and indicating an assignment of one or more of the tasks to one or more of the devices. ([Huang, Page 48, Section C, Para 1; Page 49, Section IV, Para 1; Section A, Para 1] discloses generating, by reinforcement learning (i.e machine learning process), a trained encoder-decoder scheduling model. [Page 46, Section C, Para 1; Page 46, Section A, Para 1; Page 47, Fig. 2] discloses the trained encoder-decoder scheduling model to input a combination of task graph (i.e first graph) and resource graph (i.e second graph) and outputting a task assignment of tasks to slots, which are associated or contained in hosts (i.e devices))
Regarding Claim 2
Huang discloses: wherein the generating is based on one or more first metrics each associated with computational factors of corresponding ones of the first nodes ([Huang, Page 47, Section A; Page 48, Algorithm 1 & Col 1; Page 53, Col 1, Para 2] discloses generating based on run-time metrics, such as delay and throughput, (i.e first metrics associated with computational factors), which corresponds to tasks nodes (i.e first nodes) of a task graph)
Regarding Claim 3
Huang disclose: wherein the generating is based on one or more second metrics each associated with processing factors of corresponding ones of the second nodes. ([Huang, Page 48, Algorithm 1; Page 48, Col 2, Para 3] discloses generating based on CPU (i.e second metrics associated with processing factors) of resource nodes (i.e second nodes) in a resource graph)
Regarding Claim 5
Huang discloses: wherein the generating is based on one or more fourth metrics each associated with bandwidth factors of corresponding ones of the second nodes. ([Huang, Page 47, Section B; Page 48, Algorithm 1 & Col 1] discloses generating based on delay and throughput (i.e fourth metrics associated with bandwidth), which corresponds to resource nodes (i.e second nodes) of a resource graph: “…delay is defined as the sum of the communication delays between all slots passing from source to sink. Throughput represents the number of tuples processed per second for a specific DSP job.”)
Regarding Claim 6
Huang discloses: wherein the first graph and the second graph each comprise a respective directed graph. ([Huang, Page 45, Fig. 1, Section A-B] discloses task graph (i.e first graph) and resource graph (i.e second graph) having a directed graph. Resource graph is described as an undirected graph, which is a symmetric directed graph where the edges are bidirectional)
Regarding Claim 7
Huang discloses: wherein the machine learning model comprises a graph convolutional network model. ([Huang, Page 46, Section A, Para 1-2] discloses encoder-decoder model (i.e machine learning model) comprises a Graph Convolutional Network)
Regarding Claim 9
Huang discloses: mapping the graph to hardware of a distributed computing system based on the trained model. ([Huang, Page 45-46, Section B; Page 46, Section C, Para 1; Page 46, Section A, Para 1; Page 47, Fig. 2] discloses mapping the task graph to slots (i.e hardware) of a infrastructure resources among hosts (i.e distributed computing system))
Regarding Claim 10
Claim 10 is a system claim having similar limitations of method of Claim 1, therefore it is rejected under the same rational as of Claim 1. Additionally, Claim 10 includes additional limitations below that rejected under Huang.
Huang teaches: a memory and a processor to ([Huang, Page 49, Section IV; Page 50, Section A] discloses using GitHub and CPUs, which uses memory and processors)
Regarding Claim 11
(Claim 11 recites analogous limitations to Claim 2 and therefore is rejected on the same ground as Claim 2.)
Regarding Claim 12
(Claim 12 recites analogous limitations to Claim 3 and therefore is rejected on the same ground as Claim 3.)
Regarding Claim 14
(Claim 14 recites analogous limitations to Claim 5 and therefore is rejected on the same ground as Claim 5.)
Regarding Claim 15
(Claim 15 recites analogous limitations to Claim 6 and therefore is rejected on the same ground as Claim 6.)
Regarding Claim 16
(Claim 16 recites analogous limitations to Claim 7 and therefore is rejected on the same ground as Claim 7.)
Regarding Claim 18
(Claim 18 recites analogous limitations to Claim 9 and therefore is rejected on the same ground as Claim 9.)
Regarding Claim 19
Claim 19 is a computer readable medium claim having similar limitations of method of Claim 1, therefore it is rejected under the same rational as of Claim 1. Additionally, Claim 10 includes additional limitations below that rejected under Huang.
Huang teaches: A computer readable medium including one or more instructions stored thereon and executable by a processor to: ([Huang, Page 49, Section IV; Page 50, Section A] discloses running code on GitHub and CPUs, which uses a computer medium storing instructions, processors, and memory)
Regarding Claim 20
Huang discloses: generate the trained model based on one or more first metrics each associated with computational factors of corresponding ones of the first nodes, ([Huang, Page 47, Section A; Page 48, Algorithm 1 & Col 1; Page 53, Col 1, Para 2] discloses generating based on run-time metrics, such as delay and throughput, (i.e first metrics associated with computational factors), which corresponds to tasks nodes (i.e first nodes) of a task graph)
based on one or more second metrics each associated with processing factors of corresponding ones of the second nodes, ([Huang, Page 48, Algorithm 1; Page 48, Col 2, Para 3] discloses generating based on CPU (i.e second metrics associated with processing factors) of resource nodes (i.e second nodes) in a resource graph)
and based on one or more fourth metrics each associated with bandwidth factors of corresponding ones of the second nodes. ([Huang, Page 47, Section B; Page 48, Algorithm 1 & Col 1; Page 48, Col 2, Para 2] discloses generating based on delay and throughput (i.e fourth metrics associated with bandwidth), which corresponds to resource nodes (i.e second nodes) of a resource graph: “…delay is defined as the sum of the communication delays between all slots passing from source to sink… Throughput can be calculated by recycling and allocating resources and applying back pressure mechanism iteratively.”)
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 4, 8, 11-15, 17, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (“TATA: Throughput-Aware Task Placement in Heterogeneous Stream Processing with Deep Reinforcement Learning”, hereinafter “Huang”) in view of Mandal et al. (US 20200184366 A1, hereinafter “Mandal”).
Regarding Claim 4
Huang does not explicitly discloses: wherein the generating is based on one or more third metrics each associated with output factors of corresponding ones of the first edges.
However, Mandal disclose: wherein the generating is based on one or more third metrics each associated with output factors of corresponding ones of the first edges. ([Mandal, Fig. 2a, 0034-0035, 0039] discloses generating a schedule based on third metrics associated with outputs of sub-task graphs (i.e output factors). For example, [0039] states “A processing time for two recurring sub-task graphs, assuming the resources 132 are not performing other operations until the processing of the two recurring sub-task graphs is finished, may be time T2.” Certain subgraphs depend on the output of other subgraphs in order to be processed. [Fig. 2a, 0034-0035] shows an example of this dependency in Fig. 2a where the edges (i.e first edges) of first, second, and third subgraph provide output for operation 13 to process on)
Huang and Mandal are analogous art to the present invention because they are from the same field of endeavor directed to machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the method to assign tasks to resources disclosed by Huang with generating a schedule based on output factors by Mandal. One of ordinary skill in the art would have been motivated to make this modification in order to determine which operations of a task to process first based on dependency of operations for scheduling. ([Mandal, 0039])
Regarding Claim 8
Huang in view of Mandal discloses: wherein the generating is based on an existing scheduling model as a teacher model to train the model. ([Mandal, Fig. 1, 0054, 0051, 0057] discloses generating a machine learning model based on Schedule Model 120 (i.e existing schedule model) as a teacher model to train the machine learning model. The Schedule Model 120 produces a schedule and works with execution environment 130 to train the machine learning model)
Regarding Claim 13
(Claim 13 recites analogous limitations to Claim 4 and therefore is rejected on the same ground as Claim 4.)
Regarding Claim 17
(Claim 17 recites analogous limitations to Claim 8 and therefore is rejected on the same ground as Claim 8.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Amanda D. Nguyen whose telephone number is (571)270-1854. The examiner can normally be reached M-F, 7:30am to 5:00 pm ET First Fridays off, 2nd Friday 7:30 am - 4:00 pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AMANDA D NGUYEN/ Examiner, Art Unit 2127
/JEREMY L STANLEY/ Examiner, Art Unit 2127
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